Public Transport Accessibility to Business Locations such as Bars/Taverns/Pubs

Authored by: Thakoor Madhuvaishali

Duration: 150 mins

Level: Intermediate

Pre-requisite Skills: Python, Data analysis, Data cleaning, Data visualization

Scenario:

As a business owner or city planner, I want to understand the relationship between business locations (such as bars, taverns, and pubs) and public transport accessibility, so that I can make informed decisions about where to establish new businesses or how to improve public transport infrastructure to support existing businesses. By analyzing this relationship, I can identify areas where better transport options could enhance business patronage and growth, ultimately contributing to the economic development of Melbourne.

What this use case will teach you

At the end of this use case, you will:

• Learn how to use Python to analyze and visualize data, enabling you to extract meaningful insights from datasets related to business locations and public transport.
• Gain experience in mapping and geocoding, allowing you to plot business establishment locations on a map and determine their proximity to public transport options.
• Understand how to integrate multiple datasets for analysis, helping you combine information about business locations, bus stops, and tram stops to create a comprehensive and user-friendly tool for analyzing public transport accessibility to business locations.

Introduction:

Public transport accessibility is a crucial factor that can influence the success and growth of businesses. For bars, taverns, and pubs, easy access to public transport can significantly impact customer patronage, especially for those who prefer not to drive. This use case aims to explore the relationship between business locations and public transport accessibility in Melbourne by integrating and analyzing data from the City of Melbourne's Census of Land Use and Employment (CLUE) dataset (2002-2022) along with Bus Stops and Tram Stops datasets. By overlaying business establishment locations with bus and tram stops, this analysis aims to understand how accessibility to these public transport modes impacts business patronage and growth.

DATASETS LIST:

• Bar, tavern, pub patron capacity

https://data.melbourne.vic.gov.au/explore/dataset/bars-and-pubs-with-patron-capacity/information/

This dataset contains information about the locations and industry classifications of business establishments in Melbourne, covering the period from 2002 to 2022. It includes details such as the name, address, and classification (e.g., bars, taverns, pubs) of each business.

• Bus stops

https://data.melbourne.vic.gov.au/explore/dataset/bus-stops/

This dataset contains information about the locations of bus stops in the area, including their geographic coordinates (latitude and longitude). This information is crucial for helping users navigate to their desired support service locations using public transport.

• Tram tracks

https://data.melbourne.vic.gov.au/explore/dataset/tram-tracks/

This dataset contains information about the tram tracks in the area, including their routes and stops. Tram tracks are another important aspect of public transport infrastructure that can help users plan their journeys to support service locations.

Importing modules and fetching data from Open Data Melbourne

#importing all the modules
import requests
import pandas as pd
from io import StringIO
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import HeatMap
import ipywidgets as widgets
from ipywidgets import interact
from IPython.display import display

#Loading the bars and pubs dataset
base_url='https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/'
dataset_id='bars-and-pubs-with-patron-capacity'

url=f'{base_url}{dataset_id}/exports/csv'
params={'select':'*','limit':-1,'lang':'en','timezone':'UTC'}

response=requests.get(url,params=params)

if response.status_code==200:
    url_content=response.content.decode('utf-8')
    df_business=pd.read_csv(StringIO(url_content),delimiter=';')
    print(df_business.head(10))
else:
    print(f'Request failed with status code {response.status_code}')

   census_year  block_id  property_id  base_property_id  \
0         2002        11       108972            108972   
1         2002        14       103172            103172   
2         2002        15       103944            103944   
3         2002        16       103938            103938   
4         2002        17       103925            103925   
5         2002        33       105937            105937   
6         2002        35       102143            102143   
7         2002        37       102158            102158   
8         2002        42       105392            105392   
9         2002        43       108069            108069   

                               building_address  clue_small_area  \
0           10-22 Spencer Street MELBOURNE 3000  Melbourne (CBD)   
1         31-39 Elizabeth Street MELBOURNE 3000  Melbourne (CBD)   
2          277-279 Flinders Lane MELBOURNE 3000  Melbourne (CBD)   
3              187 Flinders Lane MELBOURNE 3000  Melbourne (CBD)   
4          121-123 Flinders Lane MELBOURNE 3000  Melbourne (CBD)   
5  435-443 Little Collins Street MELBOURNE 3000  Melbourne (CBD)   
6         258-274 Collins Street MELBOURNE 3000  Melbourne (CBD)   
7           86-88 Collins Street MELBOURNE 3000  Melbourne (CBD)   
8            120-138 King Street MELBOURNE 3000  Melbourne (CBD)   
9           131-141 Queen Street MELBOURNE 3000  Melbourne (CBD)   

                trading_name  \
0              Explorers Inn   
1            Connells Tavern   
2                   De Biers   
3              Adelphi Hotel   
4                     Velour   
5     The Kitchen Restaurant   
6   Australia On Collins Bar   
7         Dr Martin's Tavern   
8         The Exchange Hotel   
9  The Turf Bar & Restaurant   

                                    business_address  number_of_patrons  \
0                10-22 Spencer Street MELBOURNE 3000                 50   
1                 35 Elizabeth Street MELBOURNE 3000                350   
2  Unit 1, Basement , 277 Flinders Lane MELBOURNE...                400   
3                   187 Flinders Lane MELBOURNE 3000                 80   
4  Unit 1, Gnd & Bmt , 121 Flinders Lane MELBOURN...                350   
5  LwrGround , 25 Little Collins Street MELBOURNE...                200   
6  Shop 121, Basement , 260 Collins Street MELBOU...                 11   
7                  86A Collins Street MELBOURNE 3000                291   
8       Part Ground , 120 King Street MELBOURNE 3000                300   
9  Unit 1, LwrGround , 131 Queen Street MELBOURNE...                400   

    longitude   latitude                                 location  
0  144.955254 -37.820511   -37.82051068881513, 144.95525416628004  
1  144.964322 -37.817426     -37.81742610667125, 144.964321660097  
2  144.965307 -37.817242      -37.81724194023457, 144.96530699086  
3  144.968385 -37.816360    -37.81635974400108, 144.9683846004515  
4  144.970523 -37.815674  -37.815673646380745, 144.97052296371248  
5  144.960107 -37.816397  -37.816397070511165, 144.96010676156988  
6  144.964919 -37.815415   -37.81541503501324, 144.96491857864146  
7  144.970594 -37.813938    -37.8139380932959, 144.97059378480083  
8  144.956417 -37.817034   -37.81703382441968, 144.95641679014355  
9  144.960543 -37.815497  -37.815496733626624, 144.96054302812774  

#Loading the bus stops dataset
base_url='https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/'
dataset_id='bus-stops'


url=f'{base_url}{dataset_id}/exports/csv'
params={'select':'*','limit':-1,'lang':'en','timezone':'UTC'}

response=requests.get(url,params=params)

if response.status_code==200:
    url_content=response.content.decode('utf-8')
    df_bus_stop=pd.read_csv(StringIO(url_content),delimiter=';')
    print(df_bus_stop.head(10))
else:
    print(f'Request failed with status code {response.status_code}')

                              geo_point_2d  \
0   -37.80384165792465, 144.93239283833262   
1    -37.81548699581418, 144.9581794249902   
2   -37.81353897396532, 144.95728334230756   
3   -37.82191394843844, 144.95539345270072   
4   -37.83316401267591, 144.97443745130263   
5   -37.79436108568101, 144.92998424529242   
6  -37.817452093555325, 144.96168480565794   
7    -37.82146476463953, 144.9303191551562   
8  -37.837547087144706, 144.98191138368836   
9  -37.812490976626215, 144.95370614040704   

                                           geo_shape  prop_id  addresspt1  \
0  {"coordinates": [144.93239283833262, -37.80384...        0   76.819824   
1  {"coordinates": [144.9581794249902, -37.815486...        0   21.561304   
2  {"coordinates": [144.95728334230756, -37.81353...        0   42.177187   
3  {"coordinates": [144.95539345270072, -37.82191...        0   15.860434   
4  {"coordinates": [144.97443745130263, -37.83316...        0    0.000000   
5  {"coordinates": [144.92998424529242, -37.79436...        0    3.105722   
6  {"coordinates": [144.96168480565794, -37.81745...        0    7.239726   
7  {"coordinates": [144.9303191551562, -37.821464...        0   32.180664   
8  {"coordinates": [144.98191138368836, -37.83754...        0   41.441167   
9  {"coordinates": [144.95370614040704, -37.81249...        0   16.143764   

   addressp_1 asset_clas               asset_type  objectid   str_id  \
0         357    Signage  Sign - Public Transport       355  1235255   
1          83    Signage  Sign - Public Transport       600  1231226   
2         207    Signage  Sign - Public Transport       640  1237092   
3         181    Signage  Sign - Public Transport       918  1232777   
4           0    Signage  Sign - Public Transport      1029  1271914   
5         112    Signage  Sign - Public Transport      1139  1577059   
6         268    Signage  Sign - Public Transport      1263  1481028   
7         298    Signage  Sign - Public Transport      2527  1245221   
8          78    Signage  Sign - Public Transport      2922  1248743   
9          99    Signage  Sign - Public Transport      5111  1253565   

   addresspt  asset_subt                       model_desc   mcc_id  \
0     570648         NaN  Sign - Public Transport 1 Panel  1235255   
1     548056         NaN  Sign - Public Transport 1 Panel  1231226   
2     543382         NaN  Sign - Public Transport 1 Panel  1237092   
3     103975         NaN  Sign - Public Transport 1 Panel  1232777   
4          0         NaN  Sign - Public Transport 1 Panel  1271914   
5     616011         NaN  Sign - Public Transport 1 Panel  1577059   
6     527371         NaN  Sign - Public Transport 1 Panel  1481028   
7     110521         NaN  Sign - Public Transport 1 Panel  1245221   
8     107419         NaN  Sign - Public Transport 1 Panel  1248743   
9     602160         NaN  Sign - Public Transport 1 Panel  1253565   

   roadseg_id                                        descriptio model_no  
0       21673  Sign - Public Transport 1 Panel Bus Stop Type 13     P.16  
1       20184   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  
2       20186   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  
3       22174   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  
4       22708   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  
5       21693   Sign - Public Transport 1 Panel Bus Stop Type 1     P.16  
6       20171   Sign - Public Transport 1 Panel Bus Stop Type 3     P.16  
7       30638   Sign - Public Transport 1 Panel Bus Stop Type 3     P.16  
8       22245   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  
9       20030   Sign - Public Transport 1 Panel Bus Stop Type 8     P.16  

#Loading the tram tracks dataset
base_url='https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/'
dataset_id='tram-tracks'


url=f'{base_url}{dataset_id}/exports/csv'
params={'select':'*','limit':-1,'lang':'en','timezone':'UTC'}

response=requests.get(url,params=params)

if response.status_code==200:
    url_content=response.content.decode('utf-8')
    df_tram_tracks=pd.read_csv(StringIO(url_content),delimiter=';')
    print(df_tram_tracks.head(10))
else:
    print(f'Request failed with status code {response.status_code}')

                              geo_point_2d  \
0   -37.78861331868605, 144.93461561782556   
1   -37.819185576253524, 144.9610352542915   
2   -37.81837971233182, 144.95945259116502   
3    -37.81440385390417, 144.9702510191494   
4   -37.816738613153454, 144.9699088149105   
5   -37.83646719875296, 144.98204502621257   
6   -37.82412832636803, 144.97049982199576   
7  -37.797091788559044, 144.96748110523234   
8   -37.79372033756351, 144.95828640079029   
9    -37.8348813636736, 144.97472590308539   

                                           geo_shape  \
0  {"coordinates": [[[[144.934525400489, -37.7886...   
1  {"coordinates": [[[[144.96099441333, -37.81917...   
2  {"coordinates": [[[[144.959343869512, -37.8182...   
3  {"coordinates": [[[[144.969150077924, -37.8147...   
4  {"coordinates": [[[[144.970082905454, -37.8167...   
5  {"coordinates": [[[[144.982231917059, -37.8357...   
6  {"coordinates": [[[[144.970150613164, -37.8234...   
7  {"coordinates": [[[[144.967374842424, -37.7971...   
8  {"coordinates": [[[[144.958247558775, -37.7938...   
9  {"coordinates": [[[[144.974623014262, -37.8348...   

                                          descriptio    name  
0  <center><table><tr><th colspan='2' align='cent...   kml_3  
1  <center><table><tr><th colspan='2' align='cent...   kml_5  
2  <center><table><tr><th colspan='2' align='cent...   kml_6  
3  <center><table><tr><th colspan='2' align='cent...   kml_7  
4  <center><table><tr><th colspan='2' align='cent...   kml_8  
5  <center><table><tr><th colspan='2' align='cent...   kml_9  
6  <center><table><tr><th colspan='2' align='cent...  kml_16  
7  <center><table><tr><th colspan='2' align='cent...  kml_22  
8  <center><table><tr><th colspan='2' align='cent...  kml_26  
9  <center><table><tr><th colspan='2' align='cent...  kml_27  

Exploratory Analysis

The section involves the initial steps taken to understand and prepare the dataset for analysis. I have firstly explored the bar,patron, pub dataset and then proceeded to explore the bus stop and tram tracks dataset which were very similarly structured. The df.info() command provides a summary of the dataset, including the number of entries and columns, as well as the data types of each column. This summary helps in assessing the dataset's overall quality and identifying any missing or incorrect data. Finally, cleaning null values from the columns and removing columns that not related to the analysis ensures that the dataset is more concise, less bulky to manipulate and ready for further analysis

Exploring the bar,pubs and traverns dataset

df_business.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4696 entries, 0 to 4695
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   census_year        4696 non-null   int64  
 1   block_id           4696 non-null   int64  
 2   property_id        4696 non-null   int64  
 3   base_property_id   4696 non-null   int64  
 4   building_address   4696 non-null   object 
 5   clue_small_area    4696 non-null   object 
 6   trading_name       4696 non-null   object 
 7   business_address   4696 non-null   object 
 8   number_of_patrons  4696 non-null   int64  
 9   longitude          4676 non-null   float64
 10  latitude           4676 non-null   float64
 11  location           4676 non-null   object 
dtypes: float64(2), int64(5), object(5)
memory usage: 440.4+ KB

# Drop the columns that are not useful for the analysis
df_business = df_business.drop(columns=['location'])
df_business = df_business.drop(columns=['census_year'])
df_business = df_business.drop(columns=['property_id'])
df_business = df_business.drop(columns=['base_property_id'])
df_business = df_business.drop(columns=['number_of_patrons'])

#cleaning the datasets of missing values for latitude,longitude and name as these would be the crucial components for analysis
df_business = df_business.dropna(subset=['latitude', 'longitude', 'trading_name'])

df_business.head(5) # overview of the cleaned dataset

	block_id	building_address	clue_small_area	trading_name	business_address	longitude	latitude
0	11	10-22 Spencer Street MELBOURNE 3000	Melbourne (CBD)	Explorers Inn	10-22 Spencer Street MELBOURNE 3000	144.955254	-37.820511
1	14	31-39 Elizabeth Street MELBOURNE 3000	Melbourne (CBD)	Connells Tavern	35 Elizabeth Street MELBOURNE 3000	144.964322	-37.817426
2	15	277-279 Flinders Lane MELBOURNE 3000	Melbourne (CBD)	De Biers	Unit 1, Basement , 277 Flinders Lane MELBOURNE...	144.965307	-37.817242
3	16	187 Flinders Lane MELBOURNE 3000	Melbourne (CBD)	Adelphi Hotel	187 Flinders Lane MELBOURNE 3000	144.968385	-37.816360
4	17	121-123 Flinders Lane MELBOURNE 3000	Melbourne (CBD)	Velour	Unit 1, Gnd & Bmt , 121 Flinders Lane MELBOURN...	144.970523	-37.815674

#Plotting Business Distribution by Clue Small Area

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
df_business['clue_small_area'].value_counts().plot(kind='bar')
plt.xlabel('Clue Small Area')
plt.ylabel('Number of Businesses')
plt.title('Business Distribution by Clue Small Area')
_ = plt.xticks(rotation=45, ha='right')

The bar chart shows the number of bars, pubs, and taverns in each CLUE small area. This helps in identifying which areas have a high concentration of businesses and which areas have fewer or no such establishments for example we can see that Melbourne(CBD) has the highest number of businesses.

#Function to filter map based on Clue Small Area
def create_map(clue_area):
    # Filter the DataFrame based on the selected CLUE area
    filtered_df = df_business[df_business['clue_small_area'] == clue_area]

    # Create the map centered on the average location
    map_center = [filtered_df['latitude'].mean(), filtered_df['longitude'].mean()]
    m = folium.Map(location=map_center, zoom_start=13)

    # Add markers for each business location
    for idx, row in filtered_df.iterrows():
        folium.Marker(
            location=[row['latitude'], row['longitude']],
            popup=f"{row['trading_name']}<br>{row['business_address']}",
            tooltip=row['trading_name']
        ).add_to(m)

    # Create a custom legend
    legend_html = '''
    <div style="position: fixed;
                top: 10px; left: 10px;
                width: 150px; height: auto;
                background-color: white;
                border:2px solid grey;
                z-index: 9999;
                padding: 10px;">
    <h4>Legend</h4>
    <i class="fa fa-map-marker" style="color:blue"></i>&nbsp; Business Location<br>
    </div>
    '''

    # Add legend to the map
    m.get_root().html.add_child(folium.Element(legend_html))

    # Display the map
    display(m)


# Get the unique CLUE areas for the dropdown
clue_areas = df_business['clue_small_area'].unique()

# Use ipywidgets to create an interactive dropdown
interact(create_map, clue_area=clue_areas)

interactive(children=(Dropdown(description='clue_area', options=('Melbourne (CBD)', 'Carlton', 'Parkville', 'N…

create_map
def create_map(clue_area)

/content/<ipython-input-86-e22fdb18ea7f><no docstring>

By visualizing the business locations on a map with a dropdown filter based on CLUE small area, the purpose is to allow users (such as city planners or business owners) to interactively explore specific geographic areas and analyze the concentration of businesses in those regions. This filter helps narrow down the map view to individual areas, making it easier to focus on local business distribution and assess public transport accessibility for each area.

Exploring the bus stop dataset

df_bus_stop.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 309 entries, 0 to 308
Data columns (total 16 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   geo_point_2d  309 non-null    object 
 1   geo_shape     309 non-null    object 
 2   prop_id       309 non-null    int64  
 3   addresspt1    309 non-null    float64
 4   addressp_1    309 non-null    int64  
 5   asset_clas    309 non-null    object 
 6   asset_type    309 non-null    object 
 7   objectid      309 non-null    int64  
 8   str_id        309 non-null    int64  
 9   addresspt     309 non-null    int64  
 10  asset_subt    0 non-null      float64
 11  model_desc    309 non-null    object 
 12  mcc_id        309 non-null    int64  
 13  roadseg_id    309 non-null    int64  
 14  descriptio    309 non-null    object 
 15  model_no      309 non-null    object 
dtypes: float64(2), int64(7), object(7)
memory usage: 38.8+ KB

# Ensure the column is of type string
df_bus_stop['geo_point_2d'] = df_bus_stop['geo_point_2d'].astype(str)

# Split 'geo_point_2d' into 'latitude' and 'longitude'
df_bus_stop[['latitude', 'longitude']] = df_bus_stop['geo_point_2d'].str.split(', ', expand=True)

# Convert the new columns to float
df_bus_stop['latitude'] = df_bus_stop['latitude'].astype(float)
df_bus_stop['longitude'] = df_bus_stop['longitude'].astype(float)

# Drop the unrelated columns
df_bus_stop = df_bus_stop[['descriptio', 'model_no', 'model_desc', 'latitude', 'longitude']]

df_bus_stop.head(5)

	descriptio	model_no	model_desc	latitude	longitude
0	Sign - Public Transport 1 Panel Bus Stop Type 13	P.16	Sign - Public Transport 1 Panel	-37.803842	144.932393
1	Sign - Public Transport 1 Panel Bus Stop Type 8	P.16	Sign - Public Transport 1 Panel	-37.815487	144.958179
2	Sign - Public Transport 1 Panel Bus Stop Type 8	P.16	Sign - Public Transport 1 Panel	-37.813539	144.957283
3	Sign - Public Transport 1 Panel Bus Stop Type 8	P.16	Sign - Public Transport 1 Panel	-37.821914	144.955393
4	Sign - Public Transport 1 Panel Bus Stop Type 8	P.16	Sign - Public Transport 1 Panel	-37.833164	144.974437

Exploring the tram tracks dataset

df_tram_tracks.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 645 entries, 0 to 644
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   geo_point_2d  645 non-null    object
 1   geo_shape     645 non-null    object
 2   descriptio    645 non-null    object
 3   name          645 non-null    object
dtypes: object(4)
memory usage: 20.3+ KB

# Ensure the column is of type string
df_tram_tracks['geo_point_2d'] = df_tram_tracks['geo_point_2d'].astype(str)

# Split 'geo_point_2d' into 'latitude' and 'longitude'
df_tram_tracks[['latitude', 'longitude']] = df_tram_tracks['geo_point_2d'].str.split(', ', expand=True)

# Convert the new columns to float
df_tram_tracks['latitude'] = df_tram_tracks['latitude'].astype(float)
df_tram_tracks['longitude'] = df_tram_tracks['longitude'].astype(float)

# Drop the original 'geo_point_2d' and 'geo_shape' and 'deacriptio' columns
#Note that the description column here had data that was in html unredable format which is why it was discarded and only the name was kept
df_tram_tracks = df_tram_tracks.drop(columns=['geo_point_2d'])
df_tram_tracks = df_tram_tracks.drop(columns=['geo_shape'])
df_tram_tracks = df_tram_tracks.drop(columns=['descriptio'])

df_tram_tracks.head(5) #overview of the tram tracks dataset

	name	latitude	longitude
0	kml_3	-37.788613	144.934616
1	kml_5	-37.819186	144.961035
2	kml_6	-37.818380	144.959453
3	kml_7	-37.814404	144.970251
4	kml_8	-37.816739	144.969909

import folium

# Create a base map centered on an average location (adjust latitude and longitude for your dataset)
map_center = [df_business['latitude'].mean(), df_business['longitude'].mean()]
m = folium.Map(location=map_center, zoom_start=12)

# Add markers for each business location
for idx, row in df_business.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=f"{row['trading_name']}<br>{row['business_address']}",
        tooltip=row['trading_name'],
        icon=folium.Icon(color='red', icon='info-sign')  # Customize business markers
    ).add_to(m)

# Add markers for bus stops (assuming bus_stops DataFrame has latitude and longitude columns)
for idx, row in df_bus_stop.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=f"Bus Stop: {row['descriptio']}",
        icon=folium.Icon(color='blue', icon='bus')  # Customize bus stop markers
    ).add_to(m)

# Add markers for tram stops (assuming tram_stops DataFrame has latitude and longitude columns)
for idx, row in df_tram_tracks.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=f"Tram Stop: {row['name']}",
        icon=folium.Icon(color='green', icon='train')  # Customize tram stop markers
    ).add_to(m)


# Create a legend using HTML
legend_html = '''
<div style="position: fixed;
     bottom: 30px; left: 30px; width: 120px; height: 100px;
     background-color: white; border:2px solid grey; z-index:9999; font-size:14px;
     ">
     &nbsp; <b>Legend</b> <br>
     &nbsp; <i style="color:red;">&#9679;</i> Business <br>
     &nbsp; <i style="color:blue;">&#9679;</i> Bus Stop <br>
     &nbsp; <i style="color:green;">&#9679;</i> Tram Stop <br>
</div>
'''

# Add the legend to the map
m.get_root().html.add_child(folium.Element(legend_html))

# Display the map
m

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The visualisation of this map integrates all available datasets, including the bars, cafes and oubs dataset, bus stops, and tram tracks, to create a comprehensive map showcasing the spatial distribution of these elements. By combining these datasets into a single map, the analysis provides an overview of the entire transportation and support service landscape in the area of interest.

Accessibility Analysis of Business Locations Relative to Public Transport

In this section, we conduct an accessibility analysis of business locations within the city, focusing on their proximity to public transport stops, specifically bus and tram stations. By employing the Haversine distance formula, we calculate the distance of each business to its nearest public transport stop. This data is then normalized to derive an accessibility score, which quantifies how easily customers can reach these businesses via public transport. A lower score indicates better accessibility, while a higher score signifies potential challenges in accessing the business. This analysis aims to identify areas with adequate transport access and those lacking it, providing insights that can inform urban planning and business strategies.

from math import radians, sin, cos, sqrt, atan2


# Function to calculate distance between two points
def haversine_distance(lat1, lon1, lat2, lon2):
    # Convert latitude and longitude from degrees to radians
    lat1, lon1, lat2, lon2 = radians(lat1), radians(lon1), radians(lat2), radians(lon2)

    # Haversine formula
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    r = 6371  # Radius of Earth in kilometers
    distance = r * c

    return distance

# Iterate over each business service and calculate the distance to the nearest bus stop
for index, service_row in df_business.iterrows():
    nearest_distance = float('inf')  # Initialize with a large value
    for stop_index, stop_row in df_bus_stop.iterrows():
        distance = haversine_distance(service_row['latitude'], service_row['longitude'],
                                       stop_row['latitude'], stop_row['longitude'])
        if distance < nearest_distance:
            nearest_distance = distance
    # Update the support service DataFrame with the nearest distance to a bus stop
    df_business.at[index, 'nearest_bus_stop_distance'] = nearest_distance

# Iterate over each business service and calculate the distance to the nearest tram stop
for index, service_row in df_business.iterrows():
    nearest_distance = float('inf')  # Initialize with a large value
    for stop_index, stop_row in df_tram_tracks.iterrows():
        distance = haversine_distance(service_row['latitude'], service_row['longitude'],
                                       stop_row['latitude'], stop_row['longitude'])
        if distance < nearest_distance:
            nearest_distance = distance
    # Update the business location DataFrame with the nearest distance to a tram
    # stop
    df_business.at[index, 'nearest_tram_track_distance'] = nearest_distance

This code calculates the distance from each support service location to the nearest bus stop and tram track using the Haversine formula to account for the Earth's curvature. It iterates through each business location and calculates the distance to each bus stop and tram track, updating the DataFrame with the nearest distances. This information can be used for mapping and analysis, providing insights into the accessibility of support services via public transport.

# Normalize the nearest bus stop distance
df_business['normalized_bus_stop_distance'] = (df_business['nearest_bus_stop_distance'] - df_business['nearest_bus_stop_distance'].min()) / (df_business['nearest_bus_stop_distance'].max() - df_business['nearest_bus_stop_distance'].min())

# Normalize the nearest tram track distance
df_business['normalized_tram_track_distance'] = (df_business['nearest_tram_track_distance'] - df_business['nearest_tram_track_distance'].min()) / (df_business['nearest_tram_track_distance'].max() - df_business['nearest_tram_track_distance'].min())

# Combine these normalized distances into a single accessibility score
df_business['accessibility_score'] = (df_business['normalized_bus_stop_distance'] + df_business['normalized_tram_track_distance']) / 2

import folium

# Create a base map
melbourne_map = folium.Map(location=[-37.8136, 144.9631], zoom_start=12)

# Add business locations to the map
for idx, row in df_business.iterrows():
    folium.CircleMarker(location=[row['latitude'], row['longitude']],
                        radius=5,
                        popup=f"Accessibility Score: {row['accessibility_score']:.2f}",
                        color='blue' if row['accessibility_score'] > 0.5 else 'red',
                        fill=True).add_to(melbourne_map)

# Create a custom legend
legend_html = '''
<div style="position: fixed;
            top: 10px; left: 10px;
            width: 150px; height: auto;
            background-color: white;
            border:2px solid grey;
            z-index: 9999;
            padding: 10px;">
<h4>Legend</h4>
<i style="background: blue; border-radius: 50%; width: 12px; height: 12px; display: inline-block;"></i> Accessibility Score > 0.5<br>
<i style="background: red; border-radius: 50%; width: 12px; height: 12px; display: inline-block;"></i> Accessibility Score ≤ 0.5<br>
</div>
'''

# Add legend to the map
melbourne_map.get_root().html.add_child(folium.Element(legend_html))

# Display the map
melbourne_map

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Explanation of Accessibility Score

Low Scores (e.g., close to 0): Businesses in areas well-served by public transport, providing easy access for customers, staff, and deliveries.

High Scores (e.g., close to 1): Businesses located in areas with limited public transport access, which may deter customers or make commuting more difficult.

Interpretation of Accessibility Score Values

Value of 0.11:

An accessibility score of 0.11 suggests that the business is relatively close to public transport stops. This score implies that either the distance to the nearest bus or tram stop is significantly short, indicating good access to public transport.

Value of 0.54:

An accessibility score of 0.54 indicates that the business is farther from public transport stops. This score suggests that the business may be less accessible, meaning it is relatively far from the nearest bus or tram stop. It may require a longer walk for customers or employees to reach public transport.

Here, we are looking at the number of businesses within walking distance to bus stops and tram tracks.

from math import radians, sin, cos, sqrt, atan2

# Function to calculate the haversine distance between two coordinates
def haversine_distance(lat1, lon1, lat2, lon2):
    # Convert latitude and longitude from degrees to radians
    lat1, lon1, lat2, lon2 = radians(lat1), radians(lon1), radians(lat2), radians(lon2)

    # Haversine formula to calculate the distance
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    r = 6371  # Radius of the Earth in kilometers
    distance = r * c  # Distance in kilometers
    return distance * 1000  # Convert to meters

# Define the walking distance threshold (500 meters)
walking_distance_threshold = 500

# Create columns to store if a business is within walking distance of a bus or tram stop
df_business['within_500m_bus'] = False
df_business['within_500m_tram'] = False

# Iterate over each business and check proximity to bus stops
for index, business in df_business.iterrows():
    for _, bus_stop in df_bus_stop.iterrows():
        distance = haversine_distance(business['latitude'], business['longitude'],
                                      bus_stop['latitude'], bus_stop['longitude'])
        if distance <= walking_distance_threshold:
            df_business.at[index, 'within_500m_bus'] = True
            break  # No need to check further bus stops for this business

# Iterate over each business and check proximity to tram stops
for index, business in df_business.iterrows():
    for _, tram_stop in df_tram_tracks.iterrows():
        distance = haversine_distance(business['latitude'], business['longitude'],
                                      tram_stop['latitude'], tram_stop['longitude'])
        if distance <= walking_distance_threshold:
            df_business.at[index, 'within_500m_tram'] = True
            break  # No need to check further tram stops for this business

# Now, let's calculate the total number of businesses within 500 meters of any public transport stop
df_business['within_500m_any'] = df_business['within_500m_bus'] | df_business['within_500m_tram']

# Get the count of businesses within walking distance of public transport
within_500m_count = df_business['within_500m_any'].sum()

# Print the results
print(f"Total number of businesses within 500 meters of any public transport stop: {within_500m_count}")
print(f"Businesses near bus stops: {df_business['within_500m_bus'].sum()}")
print(f"Businesses near tram stops: {df_business['within_500m_tram'].sum()}")

Total number of businesses within 500 meters of any public transport stop: 4676
Businesses near bus stops: 4075
Businesses near tram stops: 4662

High Accessibility: The high numbers in all three categories indicate that the majority of businesses have good access to public transport, which is beneficial for customers and employees alike. Transport Planning: These insights can help local authorities and planners understand the effectiveness of public transport coverage and may guide future improvements or expansions.